Codes
Tick Chart for MetaTrader 4
The presented indicator plots a fully-functional tick chart similar to the standard price charts, with the ability of the analysis using all the MetaTrader features
Articles
Neural Networks in Trading: Adaptive Periodic Segmentation (LightGTS) for MetaTrader 5
We invite you to learn about the innovative technique of adaptive patching — a method for flexibly segmenting time series while taking their internal periodicity into account. We will also look at an efficient encoding technique that preserves important semantic characteristics when working with
Neural Networks in Trading: An Intelligent Forecast Pipeline (Conclusion) for MetaTrader 5
The article provides a fascinating look at how SwiGLU embedding reveals hidden market patterns, and how a sparse Mixture of Experts within a Decoder-Only Transformer makes forecasts more accurate at reasonable computational cost. We take an in-depth look at the integration of Time‑MoE into MQL5 and
Neural Networks in Trading: An Intelligent Forecast Pipeline (Sparse Mixture of Experts) for MetaTrader 5
We invite you to explore the practical implementation of a sparse mixture of experts block for time series in the OpenCL computing environment. This article provides a step-by-step explanation of how masked multi-window convolution works, as well as how gradient-based training is organized in the
Neural Networks in Trading: An Intelligent Forecast Pipeline (Time-MoE) for MetaTrader 5
We invite you to explore the modern Time-MoE framework, which has been adapted for time series forecasting tasks. In this article, we will implement the key components of the architecture step by step, providing explanations and practical examples along the way. This approach will allow you not only
Neural Networks in Trading: A Cross-Domain Time Series Forecasting Framework (Conclusion) for MetaTrader 5
The article focuses on the practical implementation of the TimeFound model for time series forecasting. The key stages of implementing the framework's main approaches using MQL5 are examined
Neural Networks in Trading: A Cross-Domain Time Series Forecasting Framework (TimeFound) for MetaTrader 5
In this article, we build the core of the TimeFound intelligent model step by step, adapting it to real-world time series forecasting tasks. If you are interested in the practical implementation of neural network patching algorithms in MQL5, you have come to the right place
Neural Networks in Trading: Effective Feature Extraction for Accurate Classification (Final Part) for MetaTrader 5
The Mantis framework transforms complex time series into informative tokens and serves as a reliable foundation for an intelligent trading agent capable of operating in real time
Neural Networks in Trading: Effective Feature Extraction for Accurate Classification (Building Objects) for MetaTrader 5
Mantis is a versatile tool for in-depth time series analysis that can be flexibly scaled to accommodate any financial scenario. Learn how a combination of patching, local convolutions, and cross-attention enables a highly accurate interpretation of market patterns
Neural Networks in Trading: Effective Feature Extraction for Accurate Classification (Mantis) for MetaTrader 5
Meet Mantis — a lightweight foundation model for time series classification based on a Transformer architecture, featuring contrastive pre-training and hybrid attention that deliver record-breaking accuracy and scalability
Neural Networks in Trading: Generalizing Time Series Without Data-Specific Dependence (Core Model Modules) for MetaTrader 5
We continue our acquaintance with the Mamba4Cast framework. Today, we will delve into the practical implementation of the proposed approaches. Mamba4Cast was designed not for lengthy warm-up on every new time series, but for immediate deployment. Thanks to the concept of Zero-Shot Forecasting, the